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Target Data Platform Independence and AI readiness leveraging Contextual Metadata

One of the most common concerns heard from enterprise data leaders is platform lock-in. Once your analytics models are built and optimized for one cloud platform, moving to another — or adding a new one — can require costly re-engineering. This can slow innovation, limit flexibility, and trap organizations into vendor dependencies that may not align with future strategies.


The Knowledge Base Processor (KBP) was designed from the ground up to eliminate that risk by generating clean, platform-independent ANSI SQL. This approach offers a level of portability that’s rare in SAP S/4 HANA analytics migration tools. Additionally, the underutilized metadata gets transformed into a contextual metadata powering AI applications.


Here's how it works:


A. Business Value: Using KBP Across modern data platforms


  • Platform-Agnostic Core — KBP first produces ANSI SQL that accurately mirrors your SAP CDS view logic, business rules, and joins.


  • Multi-Dialect Support - It can then adapt this SQL into one of 26 supported database dialects, making migration to cloud data Snowflake, Databricks, and Microsoft Fabric much easier than having to manually develop data models within each of these platforms.


  • Business logic retention - The adapted SQL isn’t just syntactically compatible — it maintains the business logic from the source, ensuring that dependencies on SMEs is cut down drastically.


The business benefits of this flexibility are significant:

  • Future-Proofing – Your investment in SAP-to-cloud migration today won’t lock you into a single platform tomorrow.

  • Multi-Cloud Strategies – If different departments or geographies use different platforms, KBP supports all of them without extra development overhead.

  • Rapid Experimentation – You can test workloads on new platforms without rebuilding models from scratch.


Consider a scenario where your primary analytics warehouse is Snowflake, but you want to explore Databricks for AI-driven use cases or Microsoft Fabric when upgrading from legacy Azure data platforms. With KBP, the same source logic can be adapted and deployed across all three in less than half the time (very conservatively) it would take to manually rebuild.


Target Data Platform Independence and Contextual Metadata


B. Technical Capability : Metadata as a Goldmine-  How KBP Prepares SAP Data for AI & Large Language Models


In most SAP data migrations, metadata is treated as a secondary output — a byproduct of the real goal: moving the data itself. But in the age of AI and Large Language Models (LLMs), metadata is no longer just documentation; it’s a strategic asset for context.


The Knowledge Base Processor (KBP) elevates metadata to first-class citizen status in the migration process. Alongside generating business-ready SQL, KBP automatically produces a rich metadata layer that’s machine-readable, AI-friendly, and standardized.


Rich Contextual Metadata Capture

When KBP processes a CDS view, it captures:

  • Technical & Business Column Names – ensuring both human and machine interpretability.

  • Data Types & Tables Usage – critical for developing context and when querying using natural language.

  • Dependencies & Relationships – mapping how each object connects to others.

  • Object Headers & Descriptions – view names, sizes, classes, and additional metadata context.


This isn’t a static report — it’s structured knowledge of your SAP S/4 HANA data landscape.


AI-Ready Formatting

By default, KBP stores metadata in JSON, which is lightweight, structured, and directly consumable by AI models. It can also output in CSV, Parquet, or BSON, depending on downstream requirements.

Why JSON matters:

  • LLM Readiness – Large language models can ingest JSON directly for fine-tuning or semantic querying.

  • Interoperability – Works seamlessly across Snowflake, Databricks, Microsoft Fabric, and other modern data platforms.

  • Automation – Enables metadata-driven workflows like automatic report generation or schema validation.


Real-World AI Use Cases

Organizations using KBP’s metadata layer can:

  • Build natural language query interfaces for SAP data.

  • Enable semantic search across their enterprise datasets.

  • Potential integration with existing data cataloging tools for enhanced documentation.

  • Feed AI models with context-rich schemas to improve accuracy in analytics automation.


Conclusion:

By turning metadata into a living, query-able, and AI-ready resource, KBP ensures that your SAP migration isn’t just about moving data — it’s about preparing your organization for the next generation of analytics and decision-making.


The result? You leave migration not just with clean data in a new platform, but with a strategic metadata asset that powers innovation long after the project is complete.


indigoChart helps organizations adopt a modern and forward-looking migration approach ensures that your data infrastructure can grow and evolve with your business, setting you up for long-term success.

If these challenges sound familiar, let us help you take the next step towards a sustainable SAP modernization strategy. Explore our SAP solutions right here.


Visit us at www.indigoChart.com or drop us a line at hello@indigochart.com




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